Malloy 
The Tao of Malloy
NTSB Flight Database
Name Game
You can find the complete source code for this model here.
The Data set consits of name, gender, state and year with the number of people that were born with that name in that gender, state and year.
query: table('bigquery-public-data.usa_names.usa_1910_2013')->{ top: 10 project: * }
| state | gender | year | name | number |
|---|---|---|---|---|
| AK | F | 1,910 | Elsie | 6 |
| AL | F | 1,910 | Annie | 482 |
| AL | F | 1,910 | Katie | 84 |
| AL | F | 1,910 | Alma | 82 |
| AL | F | 1,910 | Thelma | 65 |
| AL | F | 1,910 | Ellen | 38 |
| AL | F | 1,910 | Rebecca | 35 |
| AL | F | 1,910 | Estella | 21 |
| AL | F | 1,910 | Corine | 17 |
| AL | F | 1,910 | Margie | 17 |
[ { "state": "AK", "gender": "F", "year": 1910, "name": "Elsie", "number": 6 }, { "state": "AL", "gender": "F", "year": 1910, "name": "Annie", "number": 482 }, { "state": "AL", "gender": "F", "year": 1910, "name": "Katie", "number": 84 }, { "state": "AL", "gender": "F", "year": 1910, "name": "Alma", "number": 82 }, { "state": "AL", "gender": "F", "year": 1910, "name": "Thelma", "number": 65 }, { "state": "AL", "gender": "F", "year": 1910, "name": "Ellen", "number": 38 }, { "state": "AL", "gender": "F", "year": 1910, "name": "Rebecca", "number": 35 }, { "state": "AL", "gender": "F", "year": 1910, "name": "Estella", "number": 21 }, { "state": "AL", "gender": "F", "year": 1910, "name": "Corine", "number": 17 }, { "state": "AL", "gender": "F", "year": 1910, "name": "Margie", "number": 17 } ]
SELECT base.state as state, base.gender as gender, base.year as year, base.name as name, base.number as number FROM `bigquery-public-data.usa_names.usa_1910_2013` as base LIMIT 10
The command above says query the table 'bigquery-public-data.usa_names.usa_1910_2013' and project (show)
all the columns for the first 10 rows.
Grouping a query
In SQL there are basically two kinds of SELECT commands: SELECT ... GROUP BY and SELECT without a grouping.
In malloy, these are two different commands. The command in malloy for SELECT ... GROUP BY is group_by. Since number
and year are reserved words, we have to quote the names with back-tics.
query: table('bigquery-public-data.usa_names.usa_1910_2013')->{ group_by: name aggregate: population is `number`.sum() }
| name | population |
|---|---|
| James | 4,942,431 |
| John | 4,834,422 |
| Robert | 4,718,787 |
| Michael | 4,297,230 |
| William | 3,822,209 |
| Mary | 3,737,679 |
| David | 3,549,801 |
| Richard | 2,531,924 |
| Joseph | 2,472,917 |
| Charles | 2,244,693 |
| Thomas | 2,212,781 |
| Christopher | 1,987,490 |
| Daniel | 1,842,964 |
| Patricia | 1,569,570 |
| Matthew | 1,538,689 |
| Elizabeth | 1,492,404 |
| Jennifer | 1,463,145 |
| Linda | 1,447,601 |
| Barbara | 1,424,203 |
| Donald | 1,402,666 |
| Anthony | 1,385,453 |
| Paul | 1,355,711 |
| Mark | 1,339,371 |
| George | 1,321,652 |
| Steven | 1,269,627 |
| Kenneth | 1,259,720 |
| Andrew | 1,228,080 |
| Edward | 1,210,315 |
| Joshua | 1,163,481 |
| Brian | 1,156,495 |
| Kevin | 1,148,680 |
| Margaret | 1,120,766 |
| Susan | 1,108,712 |
| Ronald | 1,074,141 |
| Timothy | 1,055,792 |
| Dorothy | 1,052,570 |
| Jessica | 1,038,359 |
| Jason | 1,009,416 |
| Sarah | 1,006,934 |
| Betty | 985,364 |
| Nancy | 984,185 |
| Karen | 982,192 |
| Jeffrey | 969,093 |
| Lisa | 962,981 |
| Ryan | 907,969 |
| Gary | 896,637 |
| Helen | 895,428 |
| Sandra | 870,958 |
| Nicholas | 867,231 |
| Eric | 862,204 |
| Jacob | 848,518 |
| Stephen | 844,930 |
| Ashley | 841,427 |
| Donna | 826,714 |
| Kimberly | 826,275 |
| Frank | 818,197 |
| Carol | 817,290 |
| Jonathan | 803,723 |
| Michelle | 803,162 |
| Larry | 801,398 |
| Emily | 779,755 |
| Amanda | 769,292 |
| Scott | 765,844 |
| Justin | 757,273 |
| Laura | 745,892 |
| Melissa | 745,498 |
| Raymond | 744,284 |
| Deborah | 735,869 |
| Brandon | 735,849 |
| Stephanie | 732,651 |
| Rebecca | 728,799 |
| Ruth | 728,678 |
| Anna | 719,803 |
| Sharon | 718,701 |
| Kathleen | 703,265 |
| Cynthia | 703,175 |
| Gregory | 701,504 |
| Shirley | 686,079 |
| Samuel | 681,358 |
| Amy | 673,234 |
| Benjamin | 664,328 |
| Patrick | 655,434 |
| Angela | 653,663 |
| Jack | 634,113 |
| Virginia | 621,448 |
| Jerry | 618,105 |
| Dennis | 610,894 |
| Catherine | 606,029 |
| Brenda | 604,067 |
| Alexander | 598,944 |
| Katherine | 593,220 |
| Pamela | 591,307 |
| Nicole | 576,870 |
| Tyler | 576,031 |
| Christine | 574,120 |
| Henry | 571,539 |
| Walter | 558,026 |
| Peter | 553,789 |
| Douglas | 552,666 |
| Janet | 550,557 |
[ { "name": "James", "population": 4942431 }, { "name": "John", "population": 4834422 }, { "name": "Robert", "population": 4718787 }, { "name": "Michael", "population": 4297230 }, { "name": "William", "population": 3822209 }, { "name": "Mary", "population": 3737679 }, { "name": "David", "population": 3549801 }, { "name": "Richard", "population": 2531924 }, { "name": "Joseph", "population": 2472917 }, { "name": "Charles", "population": 2244693 }, { "name": "Thomas", "population": 2212781 }, { "name": "Christopher", "population": 1987490 }, { "name": "Daniel", "population": 1842964 }, { "name": "Patricia", "population": 1569570 }, { "name": "Matthew", "population": 1538689 }, { "name": "Elizabeth", "population": 1492404 }, { "name": "Jennifer", "population": 1463145 }, { "name": "Linda", "population": 1447601 }, { "name": "Barbara", "population": 1424203 }, { "name": "Donald", "population": 1402666 }, { "name": "Anthony", "population": 1385453 }, { "name": "Paul", "population": 1355711 }, { "name": "Mark", "population": 1339371 }, { "name": "George", "population": 1321652 }, { "name": "Steven", "population": 1269627 }, { "name": "Kenneth", "population": 1259720 }, { "name": "Andrew", "population": 1228080 }, { "name": "Edward", "population": 1210315 }, { "name": "Joshua", "population": 1163481 }, { "name": "Brian", "population": 1156495 }, { "name": "Kevin", "population": 1148680 }, { "name": "Margaret", "population": 1120766 }, { "name": "Susan", "population": 1108712 }, { "name": "Ronald", "population": 1074141 }, { "name": "Timothy", "population": 1055792 }, { "name": "Dorothy", "population": 1052570 }, { "name": "Jessica", "population": 1038359 }, { "name": "Jason", "population": 1009416 }, { "name": "Sarah", "population": 1006934 }, { "name": "Betty", "population": 985364 }, { "name": "Nancy", "population": 984185 }, { "name": "Karen", "population": 982192 }, { "name": "Jeffrey", "population": 969093 }, { "name": "Lisa", "population": 962981 }, { "name": "Ryan", "population": 907969 }, { "name": "Gary", "population": 896637 }, { "name": "Helen", "population": 895428 }, { "name": "Sandra", "population": 870958 }, { "name": "Nicholas", "population": 867231 }, { "name": "Eric", "population": 862204 }, { "name": "Jacob", "population": 848518 }, { "name": "Stephen", "population": 844930 }, { "name": "Ashley", "population": 841427 }, { "name": "Donna", "population": 826714 }, { "name": "Kimberly", "population": 826275 }, { "name": "Frank", "population": 818197 }, { "name": "Carol", "population": 817290 }, { "name": "Jonathan", "population": 803723 }, { "name": "Michelle", "population": 803162 }, { "name": "Larry", "population": 801398 }, { "name": "Emily", "population": 779755 }, { "name": "Amanda", "population": 769292 }, { "name": "Scott", "population": 765844 }, { "name": "Justin", "population": 757273 }, { "name": "Laura", "population": 745892 }, { "name": "Melissa", "population": 745498 }, { "name": "Raymond", "population": 744284 }, { "name": "Deborah", "population": 735869 }, { "name": "Brandon", "population": 735849 }, { "name": "Stephanie", "population": 732651 }, { "name": "Rebecca", "population": 728799 }, { "name": "Ruth", "population": 728678 }, { "name": "Anna", "population": 719803 }, { "name": "Sharon", "population": 718701 }, { "name": "Kathleen", "population": 703265 }, { "name": "Cynthia", "population": 703175 }, { "name": "Gregory", "population": 701504 }, { "name": "Shirley", "population": 686079 }, { "name": "Samuel", "population": 681358 }, { "name": "Amy", "population": 673234 }, { "name": "Benjamin", "population": 664328 }, { "name": "Patrick", "population": 655434 }, { "name": "Angela", "population": 653663 }, { "name": "Jack", "population": 634113 }, { "name": "Virginia", "population": 621448 }, { "name": "Jerry", "population": 618105 }, { "name": "Dennis", "population": 610894 }, { "name": "Catherine", "population": 606029 }, { "name": "Brenda", "population": 604067 }, { "name": "Alexander", "population": 598944 }, { "name": "Katherine", "population": 593220 }, { "name": "Pamela", "population": 591307 }, { "name": "Nicole", "population": 576870 }, { "name": "Tyler", "population": 576031 }, { "name": "Christine", "population": 574120 }, { "name": "Henry", "population": 571539 }, { "name": "Walter", "population": 558026 }, { "name": "Peter", "population": 553789 }, { "name": "Douglas", "population": 552666 }, { "name": "Janet", "population": 550557 } ]
SELECT base.name as name, SUM(base.number) as population FROM `bigquery-public-data.usa_names.usa_1910_2013` as base GROUP BY 1 ORDER BY 2 desc
malloy compiles to SQL. The SQL query for the above command is.
SELECT base.name as name, SUM(base.number) as population FROM `bigquery-public-data.usa_names.usa_1910_2013` as base GROUP BY 1 ORDER BY 2 desc
Expressions
Expressions work much the same as they do in SQL. We can look at population over decade by using a calculation against the year.
query: table('bigquery-public-data.usa_names.usa_1910_2013')->{ top: 10 group_by: decade is FLOOR(`year`/10)*10 aggregate: population is sum(`number`) }
| decade | population |
|---|---|
| 1,950 | 37,545,138 |
| 1,960 | 35,405,002 |
| 1,990 | 33,309,594 |
| 2,000 | 33,041,629 |
| 1,980 | 32,349,146 |
| 1,970 | 29,238,092 |
| 1,940 | 27,776,913 |
| 1,920 | 21,375,400 |
| 1,930 | 19,782,373 |
| 1,910 | 13,592,350 |
[ { "decade": 1950, "population": 37545138 }, { "decade": 1960, "population": 35405002 }, { "decade": 1990, "population": 33309594 }, { "decade": 2000, "population": 33041629 }, { "decade": 1980, "population": 32349146 }, { "decade": 1970, "population": 29238092 }, { "decade": 1940, "population": 27776913 }, { "decade": 1920, "population": 21375400 }, { "decade": 1930, "population": 19782373 }, { "decade": 1910, "population": 13592350 } ]
SELECT (FLOOR(base.year/10))*10 as decade, SUM(base.number) as population FROM `bigquery-public-data.usa_names.usa_1910_2013` as base GROUP BY 1 ORDER BY 2 desc LIMIT 10
Cohorts
explore: names is from( table('bigquery-public-data.usa_names.usa_1910_2013')-> { group_by: [ decade is floor(`year`/10)*10 state gender ] aggregate: cohort_population is `number`.sum() nest: by_name is { group_by: name aggregate: population is `number`.sum() } } ){ measure: births_per_100k is FLOOR(by_name.population.sum()/cohort_population.sum() *100000) measure: total_cohort_population is cohort_population.sum() dimension: name is concat(by_name.name,'') } query: names->{ group_by: [ state gender ] aggregate: total_cohort_population nest: names is { where: name : 'Michael'|'Lloyd'|'Olivia' group_by: name aggregate: population is by_name.population.sum() group_by: births_per_100k } }
| state | gender | total_cohort_population | names | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| CA | M | 15,011,405 |
|
||||||||||||
| CA | F | 13,799,158 |
|
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| NY | M | 12,203,961 |
|
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| NY | F | 11,490,175 |
|
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| TX | M | 11,126,246 |
|
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| TX | F | 10,341,113 |
|
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| PA | M | 8,497,961 |
|
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| PA | F | 8,163,949 |
|
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| IL | M | 7,845,252 |
|
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| IL | F | 7,333,624 |
|
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| OH | M | 7,324,810 |
|
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| OH | F | 6,876,716 |
|
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| MI | M | 6,036,661 |
|
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| MI | F | 5,606,794 |
|
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| FL | M | 4,910,950 |
|
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| FL | F | 4,366,273 |
|
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| NC | M | 4,326,810 |
|
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| NJ | M | 4,315,822 |
|
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| GA | M | 4,214,825 |
|
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| NC | F | 4,113,425 |
|
[ { "state": "CA", "gender": "M", "total_cohort_population": 15011405, "names": [ { "name": "Michael", "population": 420127, "births_per_100k": 2798 }, { "name": "Lloyd", "population": 8380, "births_per_100k": 55 } ] }, { "state": "CA", "gender": "F", "total_cohort_population": 13799158, "names": [ { "name": "Olivia", "population": 33794, "births_per_100k": 244 }, { "name": "Michael", "population": 2428, "births_per_100k": 19 } ] }, { "state": "NY", "gender": "M", "total_cohort_population": 12203961, "names": [ { "name": "Michael", "population": 430732, "births_per_100k": 3529 }, { "name": "Lloyd", "population": 7568, "births_per_100k": 62 }, { "name": "Olivia", "population": 5, "births_per_100k": 0 } ] }, { "state": "NY", "gender": "F", "total_cohort_population": 11490175, "names": [ { "name": "Olivia", "population": 23219, "births_per_100k": 202 }, { "name": "Michael", "population": 1761, "births_per_100k": 16 } ] }, { "state": "TX", "gender": "M", "total_cohort_population": 11126246, "names": [ { "name": "Michael", "population": 223900, "births_per_100k": 2012 }, { "name": "Lloyd", "population": 8461, "births_per_100k": 76 }, { "name": "Olivia", "population": 6, "births_per_100k": 1 } ] }, { "state": "TX", "gender": "F", "total_cohort_population": 10341113, "names": [ { "name": "Olivia", "population": 25746, "births_per_100k": 248 }, { "name": "Michael", "population": 1420, "births_per_100k": 16 }, { "name": "Lloyd", "population": 15, "births_per_100k": 1 } ] }, { "state": "PA", "gender": "M", "total_cohort_population": 8497961, "names": [ { "name": "Michael", "population": 266380, "births_per_100k": 3134 }, { "name": "Lloyd", "population": 7874, "births_per_100k": 92 }, { "name": "Olivia", "population": 22, "births_per_100k": 2 } ] }, { "state": "PA", "gender": "F", "total_cohort_population": 8163949, "names": [ { "name": "Olivia", "population": 16292, "births_per_100k": 199 }, { "name": "Michael", "population": 910, "births_per_100k": 11 } ] }, { "state": "IL", "gender": "M", "total_cohort_population": 7845252, "names": [ { "name": "Michael", "population": 251741, "births_per_100k": 3208 }, { "name": "Lloyd", "population": 7207, "births_per_100k": 91 } ] }, { "state": "IL", "gender": "F", "total_cohort_population": 7333624, "names": [ { "name": "Olivia", "population": 16450, "births_per_100k": 224 }, { "name": "Michael", "population": 989, "births_per_100k": 17 } ] }, { "state": "OH", "gender": "M", "total_cohort_population": 7324810, "names": [ { "name": "Michael", "population": 225302, "births_per_100k": 3075 }, { "name": "Lloyd", "population": 6755, "births_per_100k": 92 } ] }, { "state": "OH", "gender": "F", "total_cohort_population": 6876716, "names": [ { "name": "Olivia", "population": 16787, "births_per_100k": 244 }, { "name": "Michael", "population": 969, "births_per_100k": 18 } ] }, { "state": "MI", "gender": "M", "total_cohort_population": 6036661, "names": [ { "name": "Michael", "population": 194851, "births_per_100k": 3227 }, { "name": "Lloyd", "population": 6130, "births_per_100k": 101 } ] }, { "state": "MI", "gender": "F", "total_cohort_population": 5606794, "names": [ { "name": "Olivia", "population": 13575, "births_per_100k": 242 }, { "name": "Michael", "population": 755, "births_per_100k": 17 } ] }, { "state": "FL", "gender": "M", "total_cohort_population": 4910950, "names": [ { "name": "Michael", "population": 138882, "births_per_100k": 2828 }, { "name": "Lloyd", "population": 2876, "births_per_100k": 58 } ] }, { "state": "FL", "gender": "F", "total_cohort_population": 4366273, "names": [ { "name": "Olivia", "population": 15208, "births_per_100k": 348 }, { "name": "Michael", "population": 691, "births_per_100k": 18 } ] }, { "state": "NC", "gender": "M", "total_cohort_population": 4326810, "names": [ { "name": "Michael", "population": 92414, "births_per_100k": 2135 }, { "name": "Lloyd", "population": 4367, "births_per_100k": 100 } ] }, { "state": "NJ", "gender": "M", "total_cohort_population": 4315822, "names": [ { "name": "Michael", "population": 165385, "births_per_100k": 3832 }, { "name": "Lloyd", "population": 1720, "births_per_100k": 41 } ] }, { "state": "GA", "gender": "M", "total_cohort_population": 4214825, "names": [ { "name": "Michael", "population": 87684, "births_per_100k": 2080 }, { "name": "Lloyd", "population": 2508, "births_per_100k": 59 } ] }, { "state": "NC", "gender": "F", "total_cohort_population": 4113425, "names": [ { "name": "Olivia", "population": 10671, "births_per_100k": 259 }, { "name": "Michael", "population": 416, "births_per_100k": 19 } ] } ]
WITH __stage0 AS ( SELECT group_set, (floor(base.year/10))*10 as decade__0, base.state as state__0, base.gender as gender__0, CASE WHEN group_set=0 THEN SUM(base.number) END as cohort_population__0, CASE WHEN group_set=1 THEN base.name END as name__1, CASE WHEN group_set=1 THEN SUM(base.number) END as population__1 FROM `bigquery-public-data.usa_names.usa_1910_2013` as base CROSS JOIN (SELECT row_number() OVER() -1 group_set FROM UNNEST(GENERATE_ARRAY(0,1,1))) GROUP BY 1,2,3,4,6 ) , __stage1 AS ( SELECT decade__0 as decade, state__0 as state, gender__0 as gender, ANY_VALUE(CASE WHEN group_set=0 THEN cohort_population__0 END) as cohort_population, ARRAY_AGG(CASE WHEN group_set=1 THEN STRUCT(name__1 as name, population__1 as population) END IGNORE NULLS ORDER BY population__1 desc ) as by_name FROM __stage0 GROUP BY 1,2,3 ORDER BY 4 desc ) , __stage2 AS ( SELECT group_set, names.state as state__0, names.gender as gender__0, CASE WHEN group_set=0 THEN CAST(( ( SUM(DISTINCT (CAST(ROUND(COALESCE(names.cohort_population,0)*(1*1.0), 9) AS NUMERIC) + (cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 1, 15)) as int64) as numeric) * 4294967296 + cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 16, 8)) as int64) as numeric)) * 0.000000001 )) - SUM(DISTINCT (cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 1, 15)) as int64) as numeric) * 4294967296 + cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 16, 8)) as int64) as numeric)) * 0.000000001) )/(1*1.0)) as FLOAT64) END as total_cohort_population__0, CASE WHEN group_set=1 THEN concat(by_name_0.name,'') END as name__1, CASE WHEN group_set=1 THEN SUM(by_name_0.population) END as population__1, CASE WHEN group_set=1 THEN FLOOR((SUM(by_name_0.population)/CAST(( ( SUM(DISTINCT (CAST(ROUND(COALESCE(names.cohort_population,0)*(1*1.0), 9) AS NUMERIC) + (cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 1, 15)) as int64) as numeric) * 4294967296 + cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 16, 8)) as int64) as numeric)) * 0.000000001 )) - SUM(DISTINCT (cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 1, 15)) as int64) as numeric) * 4294967296 + cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 16, 8)) as int64) as numeric)) * 0.000000001) )/(1*1.0)) as FLOAT64))*100000) END as births_per_100k__1 FROM (SELECT GENERATE_UUID() as __distinct_key, * FROM __stage1) as names , UNNEST(names.by_name) as by_name_0 CROSS JOIN (SELECT row_number() OVER() -1 group_set FROM UNNEST(GENERATE_ARRAY(0,1,1))) WHERE (group_set NOT IN (1) OR (group_set IN (1) AND (concat(by_name_0.name,'')='Michael')or((concat(by_name_0.name,'')='Lloyd')or(concat(by_name_0.name,'')='Olivia')))) GROUP BY 1,2,3,5 ) SELECT state__0 as state, gender__0 as gender, ANY_VALUE(CASE WHEN group_set=0 THEN total_cohort_population__0 END) as total_cohort_population, ARRAY_AGG(CASE WHEN group_set=1 THEN STRUCT(name__1 as name, population__1 as population, births_per_100k__1 as births_per_100k) END IGNORE NULLS ORDER BY population__1 desc ) as names FROM __stage2 GROUP BY 1,2 ORDER BY 3 desc
Dashboard
query: names-> name_dashboard {where: name ~ 'Mich%'}
| name | total_population | state_map | decade_graph |
|---|---|---|---|
| Michael | 4,297,230 | ||
| Michelle | 803,162 | ||
| Michele | 221,675 | ||
| Micheal | 149,601 | ||
| Michaela | 44,588 | ||
| Michel | 7,022 | ||
| Michell | 4,371 | ||
| Michal | 2,165 | ||
| Michale | 966 | ||
| Michelina | 892 | ||
| Michaella | 659 | ||
| Michela | 598 | ||
| Michaelene | 477 | ||
| Michaelangelo | 467 | ||
| Michala | 465 |
[ { "name": "Michael", "total_population": 4297230, "state_map": [ { "state": "RI", "births_per_100k": 3791 }, { "state": "AK", "births_per_100k": 3549 }, { "state": "DE", "births_per_100k": 3461 }, { "state": "VT", "births_per_100k": 3121 }, { "state": "NH", "births_per_100k": 2920 }, { "state": "ME", "births_per_100k": 2888 }, { "state": "WY", "births_per_100k": 2736 }, { "state": "NV", "births_per_100k": 2720 }, { "state": "HI", "births_per_100k": 2629 }, { "state": "CT", "births_per_100k": 2535 }, { "state": "DC", "births_per_100k": 2376 }, { "state": "MT", "births_per_100k": 2358 }, { "state": "SD", "births_per_100k": 2304 }, { "state": "MA", "births_per_100k": 2278 }, { "state": "NJ", "births_per_100k": 2273 }, { "state": "ID", "births_per_100k": 2071 }, { "state": "ND", "births_per_100k": 2035 }, { "state": "MD", "births_per_100k": 2019 }, { "state": "NE", "births_per_100k": 1972 }, { "state": "WI", "births_per_100k": 1967 }, { "state": "OR", "births_per_100k": 1950 }, { 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"births_per_100k": 46 }, { "state": "ME", "births_per_100k": 30 }, { "state": "VT", "births_per_100k": 12 }, { "state": "LA", "births_per_100k": 11 }, { "state": "MS", "births_per_100k": 8 }, { "state": "MA", "births_per_100k": 8 }, { "state": "IN", "births_per_100k": 7 }, { "state": "OK", "births_per_100k": 7 }, { "state": "MO", "births_per_100k": 7 }, { "state": "AR", "births_per_100k": 7 }, { "state": "CA", "births_per_100k": 6 }, { "state": "TN", "births_per_100k": 6 }, { "state": "FL", "births_per_100k": 6 }, { "state": "IA", "births_per_100k": 6 }, { "state": "ID", "births_per_100k": 6 }, { "state": "NY", "births_per_100k": 5 }, { "state": "WA", "births_per_100k": 5 }, { "state": "CO", "births_per_100k": 5 }, { "state": "KY", "births_per_100k": 5 }, { "state": "TX", "births_per_100k": 4 }, { "state": "GA", "births_per_100k": 4 }, { "state": "MI", "births_per_100k": 4 }, { "state": "VA", "births_per_100k": 4 }, { "state": "OR", "births_per_100k": 4 }, { "state": "OH", "births_per_100k": 4 }, { "state": "SC", "births_per_100k": 4 }, { "state": "IL", "births_per_100k": 3 }, { "state": "KS", "births_per_100k": 3 }, { "state": "CT", "births_per_100k": 3 }, { "state": "NC", "births_per_100k": 3 }, { "state": "AL", "births_per_100k": 3 }, { "state": "MN", "births_per_100k": 2 }, { "state": "MD", "births_per_100k": 2 }, { "state": "NJ", "births_per_100k": 2 }, { "state": "WI", "births_per_100k": 2 }, { "state": "PA", "births_per_100k": 1 }, { "state": "AZ", "births_per_100k": 1 } ], "decade_graph": [ { "decade": 1950, "births_per_100k": 9, "baby_gender": "Male" }, { "decade": 1960, "births_per_100k": 8, "baby_gender": "Male" }, { "decade": 1970, "births_per_100k": 6, "baby_gender": "Male" }, { "decade": 1980, "births_per_100k": 6, "baby_gender": "Male" }, { "decade": 1910, "births_per_100k": 4, "baby_gender": "Male" }, { "decade": 1940, "births_per_100k": 4, "baby_gender": "Male" }, { "decade": 1990, "births_per_100k": 4, "baby_gender": "Male" }, { "decade": 1920, "births_per_100k": 3, "baby_gender": "Male" }, { "decade": 2000, "births_per_100k": 3, "baby_gender": "Female" }, { "decade": 1970, "births_per_100k": 3, "baby_gender": "Female" }, { "decade": 1990, "births_per_100k": 3, "baby_gender": "Female" }, { "decade": 1930, "births_per_100k": 3, "baby_gender": "Male" }, { "decade": 1960, "births_per_100k": 3, "baby_gender": "Female" }, { "decade": 2000, "births_per_100k": 3, "baby_gender": "Male" }, { "decade": 1940, "births_per_100k": 2, "baby_gender": "Female" }, { "decade": 1980, "births_per_100k": 2, "baby_gender": "Female" }, { "decade": 2010, "births_per_100k": 1, "baby_gender": "Male" }, { "decade": 1950, "births_per_100k": 1, "baby_gender": "Female" }, { "decade": 2010, "births_per_100k": 0, "baby_gender": "Female" } ] }, { "name": "Michell", "total_population": 4371, "state_map": [ { "state": "MS", "births_per_100k": 21 }, { "state": "LA", "births_per_100k": 16 }, { "state": "MO", "births_per_100k": 14 }, { "state": "AR", "births_per_100k": 13 }, { "state": "NV", "births_per_100k": 10 }, { "state": "CO", "births_per_100k": 10 }, { "state": "OK", "births_per_100k": 9 }, { "state": "KY", "births_per_100k": 9 }, { "state": "KS", "births_per_100k": 9 }, { "state": "GA", "births_per_100k": 9 }, { "state": "AL", "births_per_100k": 9 }, { "state": "IN", "births_per_100k": 9 }, { "state": "SC", "births_per_100k": 8 }, { "state": "DC", "births_per_100k": 8 }, { "state": "TX", "births_per_100k": 7 }, { "state": "AZ", "births_per_100k": 7 }, { "state": "FL", "births_per_100k": 6 }, { "state": "CA", "births_per_100k": 6 }, { "state": "WA", "births_per_100k": 6 }, { "state": "NY", "births_per_100k": 6 }, { "state": "OH", "births_per_100k": 6 }, { "state": "IL", "births_per_100k": 5 }, { "state": "MI", "births_per_100k": 5 }, { "state": "MD", "births_per_100k": 5 }, { "state": "PA", "births_per_100k": 4 }, { "state": "NJ", "births_per_100k": 4 }, { "state": "WI", "births_per_100k": 4 }, { "state": "NC", "births_per_100k": 4 }, { "state": "MN", "births_per_100k": 3 }, { "state": "OR", "births_per_100k": 3 }, { "state": "TN", "births_per_100k": 3 }, { "state": "VA", "births_per_100k": 2 }, { "state": "MA", "births_per_100k": 2 }, { "state": "IA", "births_per_100k": 2 } ], "decade_graph": [ { "decade": 1970, "births_per_100k": 13, "baby_gender": "Female" }, { "decade": 1960, "births_per_100k": 7, "baby_gender": "Female" }, { "decade": 1980, "births_per_100k": 6, "baby_gender": "Female" }, { "decade": 1990, "births_per_100k": 6, "baby_gender": "Female" }, { "decade": 2000, "births_per_100k": 5, "baby_gender": "Female" }, { "decade": 2010, "births_per_100k": 2, "baby_gender": "Female" }, { "decade": 1950, "births_per_100k": 0, "baby_gender": "Female" }, { "decade": 1970, "births_per_100k": 0, "baby_gender": "Male" }, { "decade": 1990, "births_per_100k": 0, "baby_gender": "Male" } ] }, { "name": "Michal", "total_population": 2165, "state_map": [ { "state": "IL", "births_per_100k": 8 }, { "state": "UT", "births_per_100k": 8 }, { "state": "CT", "births_per_100k": 5 }, { "state": "NY", "births_per_100k": 4 }, { "state": "NJ", "births_per_100k": 4 }, { "state": "CO", "births_per_100k": 2 }, { "state": "IA", "births_per_100k": 2 }, { "state": "MD", "births_per_100k": 2 }, { "state": "CA", "births_per_100k": 1 }, { "state": "OH", "births_per_100k": 1 }, { "state": "TX", "births_per_100k": 1 }, { "state": "AL", "births_per_100k": 1 }, { "state": "FL", "births_per_100k": 1 }, { "state": "MO", "births_per_100k": 1 }, { "state": "TN", "births_per_100k": 1 }, { "state": "PA", "births_per_100k": 1 }, { "state": "MA", "births_per_100k": 1 }, { "state": "GA", "births_per_100k": 1 }, { "state": "MI", "births_per_100k": 0 } ], "decade_graph": [ { "decade": 2010, "births_per_100k": 10, "baby_gender": "Male" }, { "decade": 2000, "births_per_100k": 5, "baby_gender": "Male" }, { "decade": 1990, "births_per_100k": 4, "baby_gender": "Male" }, { "decade": 2010, "births_per_100k": 4, "baby_gender": "Female" }, { "decade": 1980, "births_per_100k": 4, "baby_gender": "Female" }, { "decade": 1970, "births_per_100k": 3, "baby_gender": "Female" }, { "decade": 1990, "births_per_100k": 3, "baby_gender": "Female" }, { "decade": 2000, "births_per_100k": 2, "baby_gender": "Female" }, { "decade": 1940, "births_per_100k": 2, "baby_gender": "Female" }, { "decade": 1980, "births_per_100k": 1, "baby_gender": "Male" }, { "decade": 1950, "births_per_100k": 1, "baby_gender": "Male" }, { "decade": 1960, "births_per_100k": 0, "baby_gender": "Male" }, { "decade": 1950, "births_per_100k": 0, "baby_gender": "Female" }, { "decade": 1960, "births_per_100k": 0, "baby_gender": "Female" }, { "decade": 1970, "births_per_100k": 0, "baby_gender": "Male" }, { "decade": 1940, "births_per_100k": 0, "baby_gender": "Male" } ] }, { "name": "Michale", "total_population": 966, "state_map": [ { "state": "UT", "births_per_100k": 4 }, { "state": "CA", "births_per_100k": 3 }, { "state": "FL", "births_per_100k": 2 }, { "state": "TX", "births_per_100k": 2 }, { "state": "IL", "births_per_100k": 2 }, { "state": "OK", "births_per_100k": 2 }, { "state": "GA", "births_per_100k": 1 }, { "state": "NY", "births_per_100k": 1 }, { "state": "MI", "births_per_100k": 1 }, { "state": "PA", "births_per_100k": 1 }, { "state": "OH", "births_per_100k": 1 }, { "state": "KY", "births_per_100k": 1 }, { "state": "TN", "births_per_100k": 1 }, { "state": "AZ", "births_per_100k": 1 }, { "state": "NC", "births_per_100k": 1 }, { "state": "IN", "births_per_100k": 1 }, { "state": "VA", "births_per_100k": 1 } ], "decade_graph": [ { "decade": 1980, "births_per_100k": 3, "baby_gender": "Male" }, { "decade": 1970, "births_per_100k": 3, "baby_gender": "Male" }, { "decade": 1950, "births_per_100k": 2, "baby_gender": "Male" }, { "decade": 2010, "births_per_100k": 2, "baby_gender": "Male" }, { "decade": 1960, "births_per_100k": 1, "baby_gender": "Male" }, { "decade": 1990, "births_per_100k": 0, "baby_gender": "Male" } ] }, { "name": "Michelina", "total_population": 892, "state_map": [ { "state": "CT", "births_per_100k": 9 }, { "state": "RI", "births_per_100k": 9 }, { "state": "MA", "births_per_100k": 8 }, { "state": "NJ", "births_per_100k": 7 }, { "state": "NY", "births_per_100k": 6 }, { "state": "PA", "births_per_100k": 1 } ], "decade_graph": [ { "decade": 1920, "births_per_100k": 12, "baby_gender": "Female" }, { "decade": 1910, "births_per_100k": 10, "baby_gender": "Female" }, { "decade": 1930, "births_per_100k": 8, "baby_gender": "Female" }, { "decade": 1940, "births_per_100k": 3, "baby_gender": "Female" }, { "decade": 1970, "births_per_100k": 1, "baby_gender": "Female" }, { "decade": 1950, "births_per_100k": 1, "baby_gender": "Female" }, { "decade": 1960, "births_per_100k": 1, "baby_gender": "Female" }, { "decade": 2000, "births_per_100k": 0, "baby_gender": "Female" }, { "decade": 1990, "births_per_100k": 0, "baby_gender": "Female" } ] }, { "name": "Michaella", "total_population": 659, "state_map": [ { "state": "MO", "births_per_100k": 6 }, { "state": "MA", "births_per_100k": 6 }, { "state": "WA", "births_per_100k": 5 }, { "state": "FL", "births_per_100k": 3 }, { "state": "NY", "births_per_100k": 3 }, { "state": "UT", "births_per_100k": 3 }, { "state": "IN", "births_per_100k": 3 }, { "state": "OH", "births_per_100k": 3 }, { "state": "NC", "births_per_100k": 3 }, { "state": "CO", "births_per_100k": 3 }, { "state": "NJ", "births_per_100k": 2 }, { "state": "CA", "births_per_100k": 2 }, { "state": "PA", "births_per_100k": 2 }, { "state": "IL", "births_per_100k": 2 }, { "state": "MI", "births_per_100k": 2 }, { "state": "GA", "births_per_100k": 1 }, { "state": "TX", "births_per_100k": 1 } ], "decade_graph": [ { "decade": 1990, "births_per_100k": 4, "baby_gender": "Female" }, { "decade": 2000, "births_per_100k": 1, "baby_gender": "Female" }, { "decade": 2010, "births_per_100k": 1, "baby_gender": "Female" }, { "decade": 1960, "births_per_100k": 0, "baby_gender": "Female" }, { "decade": 1980, "births_per_100k": 0, "baby_gender": "Female" } ] }, { "name": "Michela", "total_population": 598, "state_map": [ { "state": "MA", "births_per_100k": 12 }, { "state": "NJ", "births_per_100k": 4 }, { "state": "NY", "births_per_100k": 3 }, { "state": "CA", "births_per_100k": 2 }, { "state": "PA", "births_per_100k": 2 }, { "state": "IL", "births_per_100k": 1 }, { "state": "FL", "births_per_100k": 1 }, { "state": "OH", "births_per_100k": 1 } ], "decade_graph": [ { "decade": 1990, "births_per_100k": 5, "baby_gender": "Female" }, { "decade": 2010, "births_per_100k": 4, "baby_gender": "Female" }, { "decade": 2000, "births_per_100k": 2, "baby_gender": "Female" }, { "decade": 1980, "births_per_100k": 1, "baby_gender": "Female" }, { "decade": 1970, "births_per_100k": 0, "baby_gender": "Female" }, { "decade": 1940, "births_per_100k": 0, "baby_gender": "Female" } ] }, { "name": "Michaelene", "total_population": 477, "state_map": [ { "state": "PA", "births_per_100k": 4 }, { "state": "IL", "births_per_100k": 4 }, { "state": "IA", "births_per_100k": 2 }, { "state": "NJ", "births_per_100k": 1 }, { "state": "IN", "births_per_100k": 1 }, { "state": "MI", "births_per_100k": 1 }, { "state": "OH", "births_per_100k": 1 }, { "state": "NY", "births_per_100k": 0 } ], "decade_graph": [ { "decade": 1940, "births_per_100k": 4, "baby_gender": "Female" }, { "decade": 1960, "births_per_100k": 3, "baby_gender": "Female" }, { "decade": 1950, "births_per_100k": 2, "baby_gender": "Female" }, { "decade": 1930, "births_per_100k": 1, "baby_gender": "Female" }, { "decade": 1970, "births_per_100k": 1, "baby_gender": "Female" }, { "decade": 1980, "births_per_100k": 0, "baby_gender": "Female" } ] }, { "name": "Michaelangelo", "total_population": 467, "state_map": [ { "state": "CA", "births_per_100k": 3 }, { "state": "FL", "births_per_100k": 1 }, { "state": "NY", "births_per_100k": 1 }, { "state": "MA", "births_per_100k": 1 }, { "state": "TX", "births_per_100k": 1 }, { "state": "AZ", "births_per_100k": 1 }, { "state": "IL", "births_per_100k": 0 } ], "decade_graph": [ { "decade": 2010, "births_per_100k": 3, "baby_gender": "Male" }, { "decade": 1990, "births_per_100k": 2, "baby_gender": "Male" }, { "decade": 2000, "births_per_100k": 2, "baby_gender": "Male" }, { "decade": 1980, "births_per_100k": 1, "baby_gender": "Male" }, { "decade": 1970, "births_per_100k": 0, "baby_gender": "Male" } ] }, { "name": "Michala", "total_population": 465, "state_map": [ { "state": "NC", "births_per_100k": 9 }, { "state": "OK", "births_per_100k": 7 }, { "state": "MO", "births_per_100k": 5 }, { "state": "NE", "births_per_100k": 5 }, { "state": "VA", "births_per_100k": 5 }, { "state": "TX", "births_per_100k": 4 }, { "state": "MI", "births_per_100k": 4 }, { "state": "OH", "births_per_100k": 4 }, { "state": "GA", "births_per_100k": 3 }, { "state": "PA", "births_per_100k": 3 }, { "state": "IL", "births_per_100k": 2 }, { "state": "KY", "births_per_100k": 2 }, { "state": "MD", "births_per_100k": 2 }, { "state": "TN", "births_per_100k": 2 }, { "state": "LA", "births_per_100k": 2 }, { "state": "IN", "births_per_100k": 1 }, { "state": "CA", "births_per_100k": 1 }, { "state": "MN", "births_per_100k": 1 }, { "state": "WA", "births_per_100k": 1 }, { "state": "NY", "births_per_100k": 1 }, { "state": "FL", "births_per_100k": 0 } ], "decade_graph": [ { "decade": 1970, "births_per_100k": 5, "baby_gender": "Female" }, { "decade": 1980, "births_per_100k": 4, "baby_gender": "Female" }, { "decade": 1990, "births_per_100k": 3, "baby_gender": "Female" }, { "decade": 2000, "births_per_100k": 0, "baby_gender": "Female" } ] } ]
WITH __stage0 AS ( SELECT group_set, (floor(base.year/10))*10 as decade__0, base.state as state__0, CASE WHEN base.gender='M' THEN 'Male' ELSE 'Female' END as baby_gender__0, base.gender as gender__0, CASE WHEN group_set=0 THEN SUM(base.number) END as cohort_population__0, CASE WHEN group_set=1 THEN base.name END as name__1, CASE WHEN group_set=1 THEN SUM(base.number) END as population__1 FROM `bigquery-public-data.usa_names.usa_1910_2013` as base CROSS JOIN (SELECT row_number() OVER() -1 group_set FROM UNNEST(GENERATE_ARRAY(0,1,1))) GROUP BY 1,2,3,4,5,7 ) , __stage1 AS ( SELECT decade__0 as decade, state__0 as state, baby_gender__0 as baby_gender, gender__0 as gender, ANY_VALUE(CASE WHEN group_set=0 THEN cohort_population__0 END) as cohort_population, ARRAY_AGG(CASE WHEN group_set=1 THEN STRUCT(name__1 as name, population__1 as population) END IGNORE NULLS ORDER BY population__1 desc ) as by_name FROM __stage0 GROUP BY 1,2,3,4 ORDER BY 5 desc ) , __stage2 AS ( SELECT group_set, concat(by_name_0.name,'') as name__0, CASE WHEN group_set=0 THEN SUM(by_name_0.population) END as total_population__0, CASE WHEN group_set=1 THEN names.state END as state__1, CASE WHEN group_set=1 THEN FLOOR((SUM(by_name_0.population)/CAST(( ( SUM(DISTINCT (CAST(ROUND(COALESCE(names.cohort_population,0)*(1*1.0), 9) AS NUMERIC) + (cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 1, 15)) as int64) as numeric) * 4294967296 + cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 16, 8)) as int64) as numeric)) * 0.000000001 )) - SUM(DISTINCT (cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 1, 15)) as int64) as numeric) * 4294967296 + cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 16, 8)) as int64) as numeric)) * 0.000000001) )/(1*1.0)) as FLOAT64))*100000) END as births_per_100k__1, CASE WHEN group_set=2 THEN names.decade END as decade__2, CASE WHEN group_set=2 THEN FLOOR((SUM(by_name_0.population)/CAST(( ( SUM(DISTINCT (CAST(ROUND(COALESCE(names.cohort_population,0)*(1*1.0), 9) AS NUMERIC) + (cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 1, 15)) as int64) as numeric) * 4294967296 + cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 16, 8)) as int64) as numeric)) * 0.000000001 )) - SUM(DISTINCT (cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 1, 15)) as int64) as numeric) * 4294967296 + cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 16, 8)) as int64) as numeric)) * 0.000000001) )/(1*1.0)) as FLOAT64))*100000) END as births_per_100k__2, CASE WHEN group_set=2 THEN names.baby_gender END as baby_gender__2 FROM (SELECT GENERATE_UUID() as __distinct_key, * FROM __stage1) as names , UNNEST(names.by_name) as by_name_0 CROSS JOIN (SELECT row_number() OVER() -1 group_set FROM UNNEST(GENERATE_ARRAY(0,2,1))) WHERE concat(by_name_0.name,'') LIKE 'Mich%' GROUP BY 1,2,4,6,8 ) SELECT name__0 as name, ANY_VALUE(CASE WHEN group_set=0 THEN total_population__0 END) as total_population, ARRAY_AGG(CASE WHEN group_set=1 THEN STRUCT(state__1 as state, births_per_100k__1 as births_per_100k) END IGNORE NULLS ORDER BY births_per_100k__1 desc ) as state_map, ARRAY_AGG(CASE WHEN group_set=2 THEN STRUCT(decade__2 as decade, births_per_100k__2 as births_per_100k, baby_gender__2 as baby_gender) END IGNORE NULLS ORDER BY births_per_100k__2 desc ) as decade_graph FROM __stage2 GROUP BY 1 ORDER BY 2 desc LIMIT 15
Iconic Names by State and Gender
Calculate the births per 100K for a name in general and a name within a state. Compute and sort by a ratio to figure out relative popularity.
query: names {where: decade <1970}->{ group_by: name group_by: gender aggregate: births_per_100k nest: by_state is { group_by: state aggregate: births_per_100k } } -> { where: births_per_100k > 50 group_by: by_state.state nest: by_gender is { group_by: gender nest: by_name is { top: 15 order_by: 2 desc group_by: name aggregate: popularity is (by_state.births_per_100k - births_per_100k) /births_per_100k } } }
| state | by_gender |
|---|---|
| AK | gender F gender M |
| AL | gender F gender M |
| AR | gender F gender M |
| AZ | gender F gender M |
| CA | gender F gender M |
| CO | gender F gender M |
| CT | gender F gender M |
| DC | gender F gender M |
| DE | gender F gender M |
| FL | gender F gender M |
| GA | gender F gender M |
| HI | gender F gender M |
| IA | gender F gender M |
| ID | gender F gender M |
| IL | gender F gender M |
| IN | gender F gender M |
| KS | gender F gender M |
| KY | gender F gender M |
| LA | gender F gender M |
| MA | gender F gender M |
| MD | gender F gender M |
| ME | gender F gender M |
| MI | gender F gender M |
| MN | gender F gender M |
| MO | gender F gender M |
| MS | gender F gender M |
| MT | gender F gender M |
| NC | gender F gender M |
| ND | gender F gender M |
| NE | gender F gender M |
| NH | gender F gender M |
| NJ | gender F gender M |
| NM | gender F gender M |
| NV | gender F gender M |
| NY | gender F gender M |
| OH | gender F gender M |
| OK | gender F gender M |
| OR | gender F gender M |
| PA | gender F gender M |
| RI | gender F gender M |
| SC | gender F gender M |
| SD | gender F gender M |
| TN | gender F gender M |
| TX | gender F gender M |
| UT | gender F gender M |
| VA | gender F gender M |
| VT | gender F gender M |
| WA | gender F gender M |
| WI | gender F gender M |
| WV | gender F gender M |
| WY | gender F gender M |
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WITH __stage0 AS ( SELECT group_set, (floor(base.year/10))*10 as decade__0, base.state as state__0, CASE WHEN base.gender='M' THEN 'Male' ELSE 'Female' END as baby_gender__0, base.gender as gender__0, CASE WHEN group_set=0 THEN SUM(base.number) END as cohort_population__0, CASE WHEN group_set=1 THEN base.name END as name__1, CASE WHEN group_set=1 THEN SUM(base.number) END as population__1 FROM `bigquery-public-data.usa_names.usa_1910_2013` as base CROSS JOIN (SELECT row_number() OVER() -1 group_set FROM UNNEST(GENERATE_ARRAY(0,1,1))) GROUP BY 1,2,3,4,5,7 ) , __stage1 AS ( SELECT decade__0 as decade, state__0 as state, baby_gender__0 as baby_gender, gender__0 as gender, ANY_VALUE(CASE WHEN group_set=0 THEN cohort_population__0 END) as cohort_population, ARRAY_AGG(CASE WHEN group_set=1 THEN STRUCT(name__1 as name, population__1 as population) END IGNORE NULLS ORDER BY population__1 desc ) as by_name FROM __stage0 GROUP BY 1,2,3,4 ORDER BY 5 desc ) , __stage2 AS ( SELECT group_set, concat(by_name_0.name,'') as name__0, names.gender as gender__0, CASE WHEN group_set=0 THEN FLOOR((SUM(by_name_0.population)/CAST(( ( SUM(DISTINCT (CAST(ROUND(COALESCE(names.cohort_population,0)*(1*1.0), 9) AS NUMERIC) + (cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 1, 15)) as int64) as numeric) * 4294967296 + cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 16, 8)) as int64) as numeric)) * 0.000000001 )) - SUM(DISTINCT (cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 1, 15)) as int64) as numeric) * 4294967296 + cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 16, 8)) as int64) as numeric)) * 0.000000001) )/(1*1.0)) as FLOAT64))*100000) END as births_per_100k__0, CASE WHEN group_set=1 THEN names.state END as state__1, CASE WHEN group_set=1 THEN FLOOR((SUM(by_name_0.population)/CAST(( ( SUM(DISTINCT (CAST(ROUND(COALESCE(names.cohort_population,0)*(1*1.0), 9) AS NUMERIC) + (cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 1, 15)) as int64) as numeric) * 4294967296 + cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 16, 8)) as int64) as numeric)) * 0.000000001 )) - SUM(DISTINCT (cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 1, 15)) as int64) as numeric) * 4294967296 + cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 16, 8)) as int64) as numeric)) * 0.000000001) )/(1*1.0)) as FLOAT64))*100000) END as births_per_100k__1 FROM (SELECT GENERATE_UUID() as __distinct_key, * FROM __stage1) as names , UNNEST(names.by_name) as by_name_0 CROSS JOIN (SELECT row_number() OVER() -1 group_set FROM UNNEST(GENERATE_ARRAY(0,1,1))) WHERE names.decade<1970 GROUP BY 1,2,3,5 ) , __stage3 AS ( SELECT name__0 as name, gender__0 as gender, ANY_VALUE(CASE WHEN group_set=0 THEN births_per_100k__0 END) as births_per_100k, ARRAY_AGG(CASE WHEN group_set=1 THEN STRUCT(state__1 as state, births_per_100k__1 as births_per_100k) END IGNORE NULLS ORDER BY births_per_100k__1 desc ) as by_state FROM __stage2 GROUP BY 1,2 ORDER BY 3 desc ) , __stage4 AS ( SELECT group_set, by_state_0.state as state__0, CASE WHEN group_set IN (1,2) THEN base.gender END as gender__1, CASE WHEN group_set=2 THEN base.name END as name__2, CASE WHEN group_set=2 THEN ((by_state_0.births_per_100k-base.births_per_100k))/base.births_per_100k END as popularity__2 FROM `__stage3` as base , UNNEST(base.by_state) as by_state_0 CROSS JOIN (SELECT row_number() OVER() -1 group_set FROM UNNEST(GENERATE_ARRAY(0,2,1))) WHERE base.births_per_100k>50 GROUP BY 1,2,3,4,5 ) , __stage5 AS ( SELECT CASE WHEN group_set=2 THEN 1 ELSE group_set END as group_set, state__0 as state__0, CASE WHEN group_set IN (1,2) THEN gender__1 END as gender__1, ARRAY_AGG(CASE WHEN group_set=2 THEN STRUCT(name__2 as name, popularity__2 as popularity) END IGNORE NULLS ORDER BY popularity__2 desc LIMIT 15) as by_name__1 FROM __stage4 GROUP BY 1,2,3 ) SELECT state__0 as state, ARRAY_AGG(CASE WHEN group_set=1 THEN STRUCT(gender__1 as gender, by_name__1 as by_name) END IGNORE NULLS ORDER BY gender__1 asc ) as by_gender FROM __stage5 GROUP BY 1 ORDER BY 1 asc